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Record W2068558612 · doi:10.1353/hpu.2012.0182

Access to Breast Cancer Screening Programs for Women with Disabilities

2012· article· en· W2068558612 on OpenAlexafffundabout
Renée Proulx, Céline Mercier, Fanny Lemétayer, Sylvie Jutras, Diane Major

Bibliographic record

VenueJournal of Health Care for the Poor and Underserved · 2012
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsRoyal Victoria Hospital
FundersCanadian Institutes of Health Research
KeywordsDelphi methodBreast cancer screeningMedicineFamily medicineIdentification (biology)Breast cancerPopulationCancer screeningResource (disambiguation)Medical educationCancerGerontologyEnvironmental healthComputer scienceMammography

Abstract

fetched live from OpenAlex

BACKGROUND: The goal of this study was to identify measures to facilitate access to the Quebec Breast Cancer Screening Program for women with activity limitations, considering the barriers to screening uptake in that population. METHODS: The study was carried out in three stages. First, 124 semi-structured interviews were conducted in five regions of Quebec with five groups of key informants. The content analysis lead to the identification of 64 proposals, which were submitted to 31 experts through a two-round Delphi survey process. Finally, consultations were held with 11 resource people to determine which decision-making levels (local, regional, provincial) could play a key role in implementing the proposals. RESULTS: A strong consensus (≥80%) was achieved for 25 proposals seen as highly relevant and feasible. DISCUSSION: The implementation of such proposals could substantially improve access to screening, given the prevalence of activity limitations in the age group targeted by the program.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.837

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.152
GPT teacher head0.419
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2012
Admission routes3
Has abstractyes

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Same venueJournal of Health Care for the Poor and UnderservedSame topicDown syndrome and intellectual disability researchFrench-language works237,207